| Autor: |
Momtahen S; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada., Momtahen M; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada., Ramaseshan R; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.; Department of Medical Physics, BC Cancer, Abbotsford, BC V2S 0C2, Canada., Golnaraghi F; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada. |
| Abstrakt: |
Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient's response to NAC. |
